Challenges and Optimization Strategies of AI Applications in Supply Chain Management

Chenyuxi Zhu

doi:10.54254/2754-1169/2025.20836

Research Article

Open access

Published on 8 February 2025

Download pdf

Zhu,C. (2025). Challenges and Optimization Strategies of AI Applications in Supply Chain Management. Advances in Economics, Management and Political Sciences,164,211-216.

Export citation

Challenges and Optimization Strategies of AI Applications in Supply Chain Management

Chenyuxi Zhu *^,1,

¹ School of Intelligent Finance and Business, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/2025.20836

Abstract

As globalization accelerates and market dynamics grow increasingly complex, supply chain management has emerged as a pivotal factor in determining corporate competitiveness. Concurrently, the rapid advancement of artificial intelligence (AI) technology has positioned its application as a critical solution for addressing supply chain optimization challenges. This study reviews the challenges faced by AI applications in supply chain management, proposes feasible optimization solutions and suggestions, and aims to help enterprises correctly apply AI technology to achieve cost reduction and efficiency improvement. Research has found that AI applications currently face challenges such as high initial investment, data security, and talent shortages. To address these issues, the study focuses on three critical industries—automotive, medical, and information technology—analyzing their unique challenges and exploring targeted solutions. This article suggests that enterprises reduce investment risks, improve data access mechanisms, and strengthen talent cultivation and introduction, to promote the broader integration of artificial intelligence within supply chain operations.

Keywords

Supply Chain Optimization, Artificial Intelligence, Case Analysis

View pdf

References

[1]. Tatarczak, A. (2024). Mapping the Landscape of Artificial Intelligence in Supply Chain Management: A Bibliometric Analysis. Modern Management Review, 29(1), 43–57.

[2]. Aggarwal, M., Yadav, A., and Garg, R. K. (2024). Enabling a green supply chain with machine learning and industry 4.0: Certain investigations for research and applications. In A. Bhardwaj, P. M. Pandey, & A. Misra (Eds.), Optimization of production and industrial systems (pp. 51-65). Springer Nature Singapore. Retrieved from https://doi.org/10.1007/978-981-99-8343-8_5

[3]. Zhu, J. Q. (2024). Supply chain management innovation in the digital transformation of e-commerce enterprises. E-commerce Review, 13(4), 5187-5193.

[4]. Jelencic, J., Massri, M. Besher., Grobelnik, M., and Mladenic, D. (2024). A Multilingual Approach to Analyzing Talent Demand in a Specific Domain: Insights From Global Perspectives on Artificial Intelligence Talent Demand. IEEE Access, 12, 80115–80127.

[5]. Mohamed, H., and Mohamed, M. (2024). AI is coming for our jobs: A cross-country study of the relationship between AI and the unemployment rate. Retrieved from https://search-ebscohost-com-s.elink. xjtlu.edu.cn:443/login.aspx?direct=true&db=edsoai&AN=edsoai.on1457651579&site=eds-live&scope =site.

[6]. Das, D. D. (2022). Impacts of Artificial Intelligence on Auto Supply Chains. Retrieved from https://www.dragonsourcing.com/impacts-of-artificial-intelligence-on-auto-supply-chains/

[7]. Rayner, D. (2016). Artificial intelligence and the supply chain. In The Oxford handbook of supply chain management (pp. 172-188). Oxford University Press.

[8]. Supply chain monitoring: Audi uses artificial intelligence (AI) for sustainability. (2021). Retrieved from https://www.automotiveworld.com/news-releases/supply-chain-monitoring-audi-uses-artificial-intelligence-ai-for-sustainability/

[9]. Abdulshaheed, H. R., Mohammed Al-Juboori, S. A., Al Sayed, I. A. M., Barazanchi, I. A., Gheni, H. M., and Jaaz, Z. A. (2022). Research on optimization strategy of medical data information security and privacy. 2022 9th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Electrical Engineering, Computer Science and Informatics (EECSI), 2022 9th International Conference On, 132–136.

[10]. Hassan, M. U., Rehmani, M. H., and Chen, J., (2019). Differential privacy techniques for cyber physical systems: a survey. IEEE Communications Surveys Tutorials, 22(1), 746-789.

[11]. Margie Meacham. (2021). AI in Talent Development: Capitalize on the AI Revolution to Transform the Way You Work, Learn, and Live. Association for Talent Development.

[12]. Xiaomi Corporation, Sun Yat-sen University, and Guizhou Electronic Science and Technology Vocational College. (2024). Xiaomi partners with 135 educational institutions to establish an industry-education integration community, fostering talent development and industrial growth. Sina Finance. Retrieved from https://cj.sina.cn/articles/view/ 2323006342/8a764386019012u9s?vt=4

Cite this article

Zhu,C. (2025). Challenges and Optimization Strategies of AI Applications in Supply Chain Management. Advances in Economics, Management and Political Sciences,164,211-216.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 4th International Conference on Business and Policy Studies

Conference website: https://2025.confbps.org/

ISBN：978-1-83558-951-9(Print) / 978-1-83558-952-6(Online)

Conference date: 20 February 2025

Editor：Canh Thien Dang

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.164

ISSN：2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).